The U-Net is a type of deep convolutional neural network architecture that has commonly been used to perform accurate image segmentation and classification. The initial success in the use of U-Net for biomedical image segmentation [1]} has prompted further developments such as the H-DenseUNet for liver and tumour segmentation [2]}. U-Net performs exceedingly well when the number of features within each training sample far exceeds the total number of training samples and has also been shown to perform well for multi-scale problems as is common in many physical systems such as fluid dynamics.
